DEM-based parameter optimization and tests of digging green onions

Experiment and optimization of the potato-soil separation and conveying device for a harvester using RecurDyn-EDEM coupling simulation

Potato combine harvesters often face the challenge of balancing efficient potato–soil separation with minimizing tuber mechanical damage, which significantly affects harvest quality and economic returns. To address this issue, a dual-vibration potato–soil separation conveyor was designed based on agronomic planting parameters and soil physical characteristics. A high-fidelity DEM-MBD coupling simulation model was developed to analyze soil clod breakage behavior and potato collision-induced jumping dynamics, and to identify key operational factors influencing separation performance. The porosity was verified using computer vision combined with CT technology to ensure the model’s fidelity. Single-factor simulations and a central composite design (CCD) response surface experiment were conducted using potato damage rate and soil removal efficiency as evaluation indices. The results showed that the inclination angle α, conveying line speed V_f, and vibration frequency f were the dominant factors affecting separation efficiency and tuber integrity. Multi-objective optimization determined optimal operating parameters of α = 18.51°, V_f = 1.995 km·h⁻¹, and f = 6.22 Hz, under which soil removal efficiency reached 98.43% and the minimum damage rate was 1.60%. Field experiments using a 4U-1000 combine harvester verified the simulation results, with an average soil removal efficiency of 97.8% and an average damage rate of 1.62%. These findings confirm the accuracy of the DEM-MBD simulation model and provide theoretical guidance for optimizing separation devices in large-scale potato harvesting equipment.

Potato is one of the most important food crops worldwide, playing a critical role in global food security and agricultural production. The broken and impurity rates are important indicators for evaluating the harvesting quality of potato combine harvesting operations. To address the difficulty of achieving continuous and online detection using traditional methods, this study investigates an online monitoring approach for potato combine harvesting based on machine vision. Considering the characteristics of large material volume, severe overlap, and similar appearance features under field operating conditions, an online monitoring device suitable for potato combine harvesters was designed, along with a corresponding image acquisition and processing workflow. For the online monitoring device, an improved You Only Look Once version 11 (YOLOv11) detection model, was proposed to meet the requirements of multi-object detection in complex operating scenarios. The model incorporates Multi-Scale Depthwise Convolution (MSDConv), C2PSA_DCA (with Directional Context Attention, DCA), and Directional Selective Attention (DSA) modules, and introduces the Efficient Intersection over Union (EIoU) loss function to enhance recognition capability for broken potatoes and multiple types of impurity targets. While maintaining lightweight characteristics, the improved model demonstrates favorable detection accuracy. Field experiment results show that when the combine harvester operates at a forward speed of 3 km/h, the relative errors for broken and impurity rates are measured as 3.78% and 3.67%, respectively. Under extreme operating conditions with a speed of 4 km/h, the corresponding average relative errors rise to 8.30% and 8.72%, respectively. Overall, the online detection results exhibit satisfactory consistency with manual measurements, providing effective technical support for real-time monitoring of harvesting quality in potato combine harvesting operations. Future research will focus on expanding multi-scenario datasets under diverse soil and illumination conditions, as well as integrating detection results with adaptive control strategies to further enhance intelligent harvesting performance.

To overcome the problems of high excavation resistance and poor soil crushing performance in sweet potato harvesting operations in brick-red soil, two types of bionic drag-reducing excavating shovels were designed, inspired by the morphological characteristics of the forefoot claws of bamboo rats. A systematic analysis involving EDEM-RecurDyn coupled simulation experiments showed that compared with an ordinary flat shovel, both of the proposed bionic shovels yielded excellent operational performance in terms of drag reduction and soil crushing. The structural parameters of the shovel body were optimized using Design-Expert 13 software, with excavation resistance and soil crushing rate as evaluation indicators. The optimal parameter combination for the bionic shovels was as follows: length 221.70 mm, width 96.78 mm, and blade inclination angle 59.09°. With this combination, the average excavation resistance was 1686.83 N, and the average soil crushing rate was 85.63%. Field tests showed that the drag reduction and soil crushing effects of the two bionic shovels were essentially consistent with the simulation results. The second shovel yielded better drag reduction and soil crushing performance, and the bionic structure design was reasonable, meaning that this design could meet the requirements for sweet potato harvesting operations in brick-red soil.